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Small-world networks
In mathematics and social psychology, a small-world network is a class of random graphs where most nodes are not neighbors of one another, but most nodes can be reached from every other by a small number of hops or steps. A small world network, where nodes represent people and edges connect people that know each other, captures the small world phenomenon of strangers being linked by a mutual acquaintance. Many empirical graphs are well modeled by small-world networks. Social networks, the connectivity of the Internet, and gene networks all exhibit small-world network characteristics. A certain category of small-world networks were identified as a class of random graphs by Duncan Watts and Steven Strogatz in 1998. They noted that graphs could be classified according to their clustering coefficient and their mean-shortest path length. While many random graphs exhibit a small shortest path (varying typically as the logarithm of the number of nodes) they also usually have a small clustering coefficient. Watts and Strogatz measured that in fact many real-world networks have a small shortest path but also a clustering coefficient significantly higher than expected by random chance. Watts and Strogatz proposed a simple model of random graphs with (i) a small average shortest path and (ii) a large clustering coefficient. The first description of the crossover in the Watts-Strogatz model between a "large world" (such as a lattice) and a small-world was described by Barthelemy and Amaral in 1999. This work was followed by a large number of studies including exact results (Barrat and Weigt, 1999; Dorogovtsev and Mendes) Properties of small-world networks By virtue of the above definition, small-world networks will inevitably have high representation of cliques, and subgraphs that are a few edges shy of being cliques, i.e. small-world networks will have sub-networks that are characterized by the presence of connections between almost any two nodes within them. This follows from the requirement of a high cluster coefficient. Secondly, most pairs of nodes will be connected by at least one short path. This follows from the requirement that the mean-shortest path length be small. Additionally, there are several properties that are commonly associated with small-world networks even though they are not required for that classification. Typically there is an over-abundance of hubs - nodes in the network with a high number of connections (known as high degree). These hubs serve as the common connections mediating the short path lengths between other edges. By analogy, the small-world network of airline flights has a small mean-path length (i.e. between any two cities you are likely to have to take three or fewer flights) because many flights are routed through hub cities. This property is often analyzed by considering the fraction of nodes in the network that have a particular number of connections going into them (the degree distribution of the network). Networks with a greater than expected number of hubs will have a greater fraction of nodes with high degree, and consequently the degree distribution will be enriched at high degree values. This is known colloquially as a fat-tailed distribution. Specifically, if a network has a degree-distribution which can be fit with a power law distribution, it is taken as a sign that the network is small-world. Power law distributions have fat tails when compared to exponential distributions characteristic of random networks. These networks are known as scale-free networks. This type of network is by no means the only kind of small-world network. Graphs of very different topology can still qualify as small-world networks as long as they satisfy the two definitional requirements above. Examples of small-world networks Small-world networks have been discovered in a surprising number of natural phenomena. For example, networks http://polaris.icmb.utexas.edu/paper-pdfs/cosb-review.pdf composed of proteins with connections indicating that the proteins physically interact have power-law obeying degree distributions and are small-world. Similarly transcriptional networks in which genes correspond to nodes, and up or down-regulatory genetic influence correspond to connections are small world networks obeying power-laws http://www.jsbi.org/journal/GIW05/GIW05F030.html. There are also many other graphs which have been found to exhibit small-world properties. Examples include road maps, food chains, electric power grids, metabolite processing networks, neural networks, telephone call graphs and social influence networks. In 2004, Sara Solla et al. developed a computer model of short-term memory constructed around a small-world network http://www.newscientist.com/article.ns?id=dn5012. This model successfully demonstrated bistability, a property thought to be important in memory storage. The bistability appears to be the result of recurrent self-sustaining loops of activity after an activating pulse is given. A second pulse would turn off the system. Hence, the pulses switch the system between its bistable states. A functioning large small-world network that can be viewed and analysed by anyone is XING. It shows that of the more than 1,000,000 members worldwide hardly anyone is more than five or six nodes away from any random other person. Network robustness It is hypothesized by some researchers such as Barabasi that the prevalence of small world networks in biological systems may reflect an evolutionary advantage of such an architecture. One possibility is that small-world networks are more robust to perturbations than other network architectures. If this were the case, it would provide an advantage to biological systems that are subject to damage by mutation or viral infection. In a power law distributed small world network, deletion of a random node rarely causes a dramatic increase in mean-shortest path length (or a dramatic decrease in the clustering coefficient). This follows from the fact that most shortest paths between nodes flow through hubs, and if a peripheral node is deleted it is unlikely to interfere with passage between other peripheral nodes. For example, if the small airport in Sun Valley, Idaho was shut down, it would not increase the average number of flights that other passengers traveling in the United states would have to take to arrive at their respective destinations. That said, if random deletion of a node hits a hub by chance, the average path length can increase dramatically. This can be observed annually when northern hub airports are shut down because of snow. If Chicago's O'Hare airport shut down, many people would have to take additional flights. By contrast, in a random network, in which all nodes have roughly the same number of connections, deleting a random node is likely to increase the mean-shortest path length slightly but significantly for almost any node deleted. In this sense, random networks are vulnerable to random perturbations, whereas small-world networks are robust. However, small-world networks are vulnerable to targeted attack of hubs, whereas random networks cannot be targeted for catastrophic failure. Appropriately, viruses have evolved to interfere with the activity of hub proteins such as p53, thereby bringing about the massive changes in cellular behavior which are conducive to viral replication. Construction of small-world networks Several procedures are known to generate small-world networks from scratch. One of these methods is known as preferential attachment http://arxiv.org/abs/cond-mat/9910332. In this model, new nodes are added to a pre-existing network, and connected to each of the original nodes with a probability proportional to the number of connections each of the original nodes already had. I.e., new nodes are more likely to attach to hubs than peripheral nodes. Statistically, this method will generate a power-law distributed small-world network. Elements of this mechanism can be seen to contribute to the small-worldness of the World Wide Web. A new site is more likely to have links to major pre-existing sites, such as Google or Wikipedia than arbitrary small obscure sites. This observation is known colloquially as a rich get richer model. See also: Diffusion-limited aggregation, pattern formation Footnotes #Review article on protein interaction networks #Article on topology of mammalian transcription networks #Barabasi preferential attachment article See also * Erdős number * Scale-free network * Six degrees of Kevin Bacon * Social network References Books * * * Journal Articles * * pdf * * http://firstmonday.org/issues/issue9_9/ravid/index.html * External links * Dr. Sara Solla - Lecture & Slides: Self-Sustained Activity in a Small-World Network of Excitable Neurons * Oskar Sandberg - Paper(PDF file): Searching in a Small World. * VisualComplexity.com - A visual exploration on mapping complex networks Category:Networks Category:Graph families